Energy consumption system optimization regulation and control method based on energy elements

Abstract

The invention discloses an energy consumption system optimization regulation and control method based on energy elements, and the method comprises the steps: constructing an energy consumption right circulation price model based on the energy elements according to the physical attributes and economic attributes of the energy elements, and determining a price interval in the circulation of the energy elements; a user energy consumption optimization regulation and control model based on the energy element price is constructed by taking the price interval and the energy consumption supply and demand balance in the energy element circulation as constraint conditions and taking the minimum user energy consumption cost as an objective function; solving the user energy consumption optimization regulation and control model to obtain an optimal user energy consumption strategy under the minimum user energy consumption cost; establishing a power system network power flow model, matching energy element supply and demand according to the optimal user energy consumption strategy and the energy element circulation state in combination with the power system network constraint, determining the actual energy consumption demand of the user, and solving the power system network power flow model by taking the minimum voltage deviation as an objective function. The optimal power flow distribution of the power system network is obtained, the unification of heterogeneous energy is realized, and the optimal regulation and control method of the energy utilization system is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of energy optimization, and in particular to an energy-element-based energy-consumption system optimization and control method. Background Art

[0002] The user side is the main source of energy consumption and carbon emissions. The energy consumption behavior of users directly affects the energy efficiency, carbon emissions and renewable energy carrying capacity of the energy system. It is worth paying attention to the green and low-carbon transformation of energy driven by the user side. However, the energy system includes a variety of heterogeneous energy coupling transformations such as electricity / heat / cold / gas. Due to the lack of a unified benchmark quantitative evaluation method, it is very difficult to compare the actual value of heterogeneous energy; the energy value of distributed renewable energy and user-side flexible resources is different, and the energy value corresponding to different time periods is also different, which is difficult to calculate and analyze. If the value difference cannot be reflected, it is difficult to guide users to optimize resource allocation; the content of user interactive circulation includes energy use rights, data and other deliverables. It is difficult to support market players to participate in multi-category market circulation based on the existing electricity / power / time series information. The above problems lead to the inability to achieve efficient circulation and reasonable allocation of energy resources, which in turn hinders the green and low-carbon transformation of the energy system. Summary of the invention

[0003] Purpose of the invention: The present invention aims to provide a method for optimizing and controlling an energy consumption system with energy elements as the basic unit for the circulation of energy consumption rights.

[0004] Technical solution: The energy system optimization and control method based on energy element described in the present invention comprises the following steps: (1) According to the physical and economic properties of energy units, a price model for energy rights circulation based on energy units is constructed to determine the price range of energy unit circulation; (2) Taking the price range of energy element circulation and the balance of energy supply and demand as constraints, and minimizing the user's energy cost as the objective function, a user energy optimization control model based on energy element prices is constructed; (3) Solve the user energy consumption optimization control model and obtain the optimal user energy consumption strategy with the minimum user energy consumption cost; (4) Establish a power system network flow model. According to the optimal user energy consumption strategy and energy element flow status, combined with the power system network constraints, match the energy element supply and demand, determine the user's actual energy demand, and solve the power system network flow model with the minimum voltage deviation as the objective function to obtain the optimal flow distribution of the power system network.

[0005] Furthermore, step (1) is specifically as follows: (11) Obtain energy system parameters, natural environment parameters and social environment parameters; (12) Construct an energy meta-model based on energy system parameters, natural environment parameters, and social environment parameters E , determine the energy equivalent of the energy element ; (13) According to the energy equivalent of energy element , build an energy right circulation price model based on energy units, and determine the price range in energy unit circulation .

[0006] Furthermore, the energy metamodel E for ; ; In the formula, A is the property set of the energy element, including the physical property set of the energy element P and the economic attribute set W ; X is a set of influencing factors; are influencing factors, which represent the influencing factors of energy use system, energy form, natural environment and social environment respectively. n is the number of influencing factors; R is the attribute set of energy element A and the set of influencing factors X A set of mapping relationships between them; For X arrive P The mapping function of For X arrive W The mapping function of .

[0007] Furthermore, the energy equivalent of the energy element for ; ; In the formula, is the energy equivalent of the energy element, in kWh; The energy equivalent of the energy element The evaluation function of Heterogeneous Energy i Energy equivalent conversion function of Heterogeneous Energy i The circulation volume, in J; I It is a collection of heterogeneous energy contained in the energy element; P A collection of physical properties.

[0008] Furthermore, the physical properties of the energy element are P for ; ; ; In the formula, is the physical property of the energy element; m is the number of physical attributes; It is from the collection X To physical properties 's mapping.

[0009] Furthermore, the economic attribute set W for ; ; ; In the formula, W is a set of economic attributes; It is the economic attribute of energy element; k is the number of economic attributes; For X To economic attributes 's mapping.

[0010] Furthermore, the price range of energy yuan circulation , solved by the following formula: ; In the formula, / The energy price given by the energy provider / receiver, in yuan / kWh; / It is the change of energy utility after providing / receiving energy yuan, in yuan; / The energy cost from the perspective of the energy provider / receiver, in yuan; The price lower bound for the energy yuan provider, in yuan; is the upper limit of the price for the energy yuan receiver, in yuan; U is the energy utility set of energy elements; / It is the set of energy element expenditure costs from the perspective of energy element provider / receiver.

[0011] Furthermore, in step (2), the user energy optimization control model with energy unit price range and energy supply and demand balance as constraints and minimum user energy cost as the objective function is: ; ; ; ; ; ; In the formula, C Energy cost for users, t To control the time period sequence number, T To control the number of time slots, e is the serial number of the energy element type, E is the number of energy element types, To control the length of the time period, for t The power purchased by the user from the power grid during the period, , Respectively represent the energy element received in the energy right circulation e Power and energy supply e The power, for t The unit energy price of electricity purchased by users from the power grid during the period, for t The energy price per unit of energy received by the user in the energy rights circulation during the time period, for t The energy price per unit of energy is provided in the circulation of energy rights of users during the period. For users t Energy demand during the period, and Separate users t Fixed energy demand and adjustable energy demand in different time periods, for t The maximum upward / downward adjustment capability of the user within the time period.

[0012] Furthermore, in step (3), mixed integer linear programming, intelligent algorithm and other methods are used to solve the user energy consumption optimization control model to obtain the energy element circulation situation and the corresponding optimal user energy consumption strategy.

[0013] Furthermore, in step (4), the power system network flow model is solved with the minimum voltage deviation as the objective function to obtain the optimal power flow distribution of the power system network: ; ; ; ; In the formula, , for t Time injection nodej The active and reactive power, , for t Time period node j The active and reactive power generation power, , for t Time period node j To Node l The branch is at the head end node j The active and reactive power at , For Node j The actual energy demand of users, , Node i To Node j The branch resistance and reactance, Node j The head node set of the terminal node; Node j is the set of end nodes of the first end node, , They are t Time period node i and nodes j The square value of the voltage, is the square value of the reference voltage, is the total system voltage deviation.

[0014] Beneficial effects: Compared with the prior art, the present invention has the following significant advantages: the present invention determines the optimal power flow distribution of the power system by optimizing and regulating user energy consumption based on the flow of energy consumption rights, can effectively achieve a unified measurement of the value of heterogeneous energy, achieve optimal configuration of user energy consumption, and realize green and low-carbon energy consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a schematic diagram of the framework of the energy meta-model; Figure 2 Schematic diagram of the energy equivalent evaluation results of energy elements. DETAILED DESCRIPTION

[0016] The present invention will be further described below in conjunction with the accompanying drawings.

[0017] The energy-element-based energy system optimization and control method of the present invention comprises the following steps: (1) Obtain the set parameters of the influencing factors of the energy element.

[0018] Obtain energy system parameters such as green energy assets, system source and load power, energy form parameters of energy resources such as solar energy, wind energy, biomass energy, and hydropower, natural environment parameters such as temperature, humidity, light intensity, and wind speed, and social environment parameters such as national policies, consumer psychology, market demand, and market supply.

[0019] (2) Establish energy meta-model.

[0020] Energy element is an energy unit that can be used or traded in the energy system. It is a commodity with physical and economic attributes. It is a unity of physical and economic attributes that describes the energy right in the market. The physical attributes reflect the physical properties of one or more heterogeneous energy sources in the process of optimal configuration and regulation, while the economic attributes reflect its value in a specific environment based on the influencing factors and the physical properties of the energy element. Establish a standardized energy meta-model as follows: ; In the formula, E is a general model of energy element; ; In the formula, A is the property set of the energy element, including the physical property set P and the economic attribute set W ; X is a set of influencing factors; are influencing factors, which represent the influencing factors of energy use system, energy form, natural environment and social environment respectively; n is the number of influencing factors; R is the attribute set of energy element A and the set of influencing factors X A set of mapping relationships between them; For X arrive P The mapping function of For X arrive W The mapping function of .

[0021] (3) Evaluation of the physical properties of energy elements.

[0022] The physical properties of energy elements are affected by multiple factors such as energy use system, energy form, natural environment, social environment, etc. X Function of Establish an energy element physical property evaluation model, the physical properties of the energy element P The specific situation of energy delivery during the energy rights circulation process is described as follows: ; ; ; In the formula, P is a set of physical properties, is the physical property of the energy element; m is the number of physical attributes; It is from the collection X To physical properties 's mapping.

[0023] The energy equivalent conversion function is designed to convert heterogeneous energy into energy equivalent properties that can be fairly compared. In order to provide a unified benchmark reference standard, the energy consumed by a constant 1kW electric power for 1 hour is taken as 1 energy equivalent, which is called the energy equivalent benchmark value. It is as follows: ; ; In the formula, is the energy equivalent of the energy element, in kWh; The energy equivalent of the energy element The evaluation function of Heterogeneous Energy i Energy equivalent conversion function of Heterogeneous Energy i The circulation volume, in J; I It is a collection of heterogeneous energy contained in the energy element.

[0024] (4) Evaluation of the economic attributes of energy elements.

[0025] An energy-economy attribute evaluation model is established. Energy-economy attributes are affected by multiple factors such as energy use system, energy form, natural environment, social environment, etc., as well as physical attributes. Therefore, energy-economy attributes are described as a function of influencing factors and physical attributes, and converted into influencing factors. X The function is as follows: ; ; ; In the formula, W is a set of economic attributes; The economic attributes of energy units, including equivalent value, energy utility, economic benefits, expenditure costs, etc. k is the number of economic attributes; For X To economic attributes Considering the set P Too X function, so Import and construct and Direct description W and X The functional relationship of .

[0026] (5) Establish an energy rights circulation pricing model based on energy units.

[0027] Construct the initial price generation model as follows: ; ; ; In the formula, / The economic benefits of the energy yuan provider / receiver, in yuan; / The energy price given by the energy provider / receiver, in yuan / kWh; It is the energy equivalent of this energy unit, in kWh.

[0028] Considering that the equivalent value of energy yuan is jointly affected by multiple economic attributes, the cost analysis method is used to equate the total value of energy yuan to the energy yuan cost of the energy yuan receiver / provider, and a cost-benefit equilibrium model for calculating the equivalent value of energy yuan is established as follows: ; ; ; ; ; ; ; In the formula, / The energy equivalent benchmark price of the energy unit from the perspective of the energy unit provider / receiver, in yuan / kWh; and are the consumption surplus (profit) of the energy yuan provider / receiver, yuan; / It is the change of energy utility after providing / receiving energy yuan, in yuan; / The energy cost from the perspective of the energy provider / receiver, in yuan; U is the energy utility set of energy elements; / It is the set of energy element expenditure costs from the perspective of energy element provider / receiver.

[0029] Determine the price range in the circulation of energy yuan as follows: ; In the formula, The price lower bound for the energy yuan provider, in yuan; is the upper limit of the price of the energy yuan receiver, in yuan; obviously, if and only if Energy Yuan can only circulate when , and the price range given by both parties is ; Otherwise, the circulation fails and both parties need to re-select the energy elements for circulation.

[0030] (6) Establish a user energy optimization control model based on energy unit price; The user's energy demand is met by purchasing electricity from the power grid and various energy rights transactions based on energy elements. Therefore, the user's energy optimization scheduling model is as follows: ; ; ; ; ; ; In the formula, C Energy cost for users, t To control the time period sequence number, T To control the number of time slots, e is the serial number of the energy element type, E is the number of energy element types, To control the length of the time period, for t The power purchased by the user from the power grid during the period, , Respectively represent the energy element received in the energy right circulation e Power and energy supply e The power, for t The unit energy price of electricity purchased by users from the power grid during the period, for t The energy price per unit of energy received by the user in the energy rights circulation during the time period, for t The energy price per unit of energy is provided in the circulation of energy rights of users during the period. For users t Energy demand during the period, and Separate users tFixed energy demand and adjustable energy demand in different time periods, for t The maximum upward / downward adjustment capability of the user within the time period.

[0031] Based on the above dispatch model, users adjust the operating status of energy-consuming equipment by changing their own energy consumption behavior according to market information such as grid electricity prices and energy consumption rights circulation prices, with the goal of minimizing energy consumption costs, and then adjust the available energy demand to obtain the user's optimal energy consumption strategy under the minimum energy consumption cost, thereby achieving energy consumption strategy optimization.

[0032] (7) Calculation method of optimal power flow of power system based on energy consumption rights flow; Various energy users solve their own energy optimization scheduling models to obtain their own optimal energy strategy and energy element circulation status. The energy right circulation platform matches the supply and demand of energy elements in the system to the maximum extent based on the power system network constraints, and solves the power flow model with the goal of minimizing voltage deviation according to the actual energy demand of each user after matching. The simplified power system power flow optimization model is as follows: ; ; ; ; In the formula, , for t Time injection node j The active and reactive power, , for t Time period node j The active and reactive power generation power, , for t Time period node j To Node l The branch is at the head end node j The active and reactive power at , For Node j The actual energy demand of users, , Node i To Node j The branch resistance and reactance, Node j The head node set of the terminal node; Node j is the set of end nodes of the first end node, , They are tTime period node i and nodes j The square value of the voltage, is the square value of the reference voltage, is the total system voltage deviation.

[0033] Based on the above model, the optimal power flow distribution of the power system can be obtained by adjusting the power generation and other behaviors; thereby reducing the voltage deviation of the power system, improving the power quality and reducing the operating cost on the basis of promoting the flow of energy rights.

[0034] In order to verify the effectiveness and rationality of the energy consumption system optimization and control method based on energy elements proposed in the present invention, a 10kV distribution network was selected for verification, and two comparison methods were set to conduct a comparative analysis of the distribution network operation and user energy consumption costs under the three methods.

[0035] Based on the actual load data of a 10kV distribution network, 1.2MW, 1.6MW and 1.3MW of photovoltaic power were installed at nodes 3, 7 and 12 respectively. Nodes 6 and 10 are energy users composed of a large number of small users.

[0036] Design 2 comparative operation scenarios: (1) Surplus power grid access scenario: The electricity supply sources of energy users are divided into power purchase from the power grid and self-generated photovoltaic power. Energy users cannot trade electricity with each other. The on-grid price of surplus electricity is 0.391 yuan / kWh.

[0037] (2) Surplus power access + demand response scenario: The electricity supply sources of energy users are divided into power purchase from the power grid and self-generated photovoltaic power. Energy users cannot trade electricity with each other, and the grid-connected electricity price of surplus electricity is 0.391 yuan / kWh. The power grid company conducts two types of demand response interactions with some energy users in the two time periods of 11:00-12:00 and 17:30-18:30, including new energy consumption and peak shaving. The subsidy for new energy consumption is 0.6 yuan / kWh, and the subsidy for peak shaving is 3 yuan / kWh.

[0038] Based on the energy element model, the physical properties of the energy element in this case system are evaluated. In this case, the physical properties are the energy equivalent of the energy element. The evaluation results are as follows: Figure 2 shown.

[0039] The economic attributes of the energy cell are evaluated. Taking the evaluation results of the economic attributes of the photovoltaic energy cell during the period of 11:30-11:45 as an example, the lower limit of each price range in the following table is the unit cost price of the energy cell seller, and the upper limit of the range is the unit energy utility equivalent price (i.e. the highest bid) of the energy cell buyer.

[0040]

[0041] Based on the above-mentioned energy meta-physical and economic attribute evaluation results, each energy-consuming entity optimizes its own energy consumption behavior. After the system energy consumption rights are matched, it is iteratively optimized based on the energy consumption rights matching situation in the entire system. After the iteration is completed, the energy consumption rights circulation results shown in the following table are obtained, which shows the energy consumption rights circulation volume and circulation price between users at each node.

[0042]

[0043] Based on the above energy rights flow results, the optimal power flow is solved based on the distribution network parameters, and the optimal operation of the distribution network system can be obtained; the distribution network operation conditions of the three scenarios are shown in the following table:

[0044] Judging from the power purchase situation of the superior power grid, the power purchase amount of the superior power grid is inversely proportional to the photovoltaic consumption rate. Therefore, the power grid purchases the least amount of power in the scenario of the present invention. In the surplus power grid-connected + demand response scenario, since the power grid uses demand response interaction to adjust the energy consumption behavior to a certain extent, the power grid purchase amount is also reduced to a certain extent compared with the surplus power grid-connected scenario.

[0045] From the perspective of the grid's profitability, since the grid cannot earn the difference between the buying and selling prices of photovoltaics in the scenario of the present invention and can only collect photovoltaic grid access fees, its profitability has dropped significantly compared with the other two scenarios. In the scenario of surplus power access + demand response, the grid's profitability has also dropped due to the high demand response subsidy price.

[0046] From the perspective of the average voltage of the distribution network, the photovoltaic price is low at noon in the scenario of the present invention, and the energy users are motivated to move part of the load during the peak period of 17:00-21:00 to noon, which reduces the energy cost and the peak load of the distribution network, and improves the voltage level of the distribution network to a certain extent. In the scenario of surplus power access + demand response, the demand response behavior of the power grid also achieves peak shaving and valley filling to a certain extent, but because noon and evening are both high electricity price periods, the energy users lack the motivation to actively adjust the load, and the average voltage level of the distribution network is slightly improved compared to the surplus power access scenario.

[0047] To sum up, the present invention can realize the accurate evaluation of energy meta-attributes and the reasonable pricing of energy consumption rights circulation, reduce users' energy consumption costs, fully tap and release the potential of user-side flexibility resources, provide an effective way for massive and widely distributed small-scale energy users to participate in market circulation, greatly improve resource liquidity, promote efficient allocation and value circulation of user-side resources, and ultimately achieve green and low-carbon energy consumption.

Claims

1. An energy system optimization and control method based on energy element, characterized in that: The following steps are involved: (1) According to the physical and economic properties of energy units, a price model for energy rights circulation based on energy units is constructed to determine the price range of energy unit circulation; (2) Taking the price range of energy element circulation and the balance of energy supply and demand as constraints, and minimizing the user's energy cost as the objective function, a user energy optimization control model based on energy element prices is constructed; (3) Solve the user energy consumption optimization control model and obtain the optimal user energy consumption strategy with the minimum user energy consumption cost; (4) Establish a power system network flow model. According to the optimal user energy consumption strategy and energy element flow status, combined with the power system network constraints, match the energy element supply and demand, determine the user's actual energy demand, and solve the power system network flow model with the minimum voltage deviation as the objective function to obtain the optimal flow distribution of the power system network.

2. The energy system optimization and control method based on energy element according to claim 1 is characterized in that: Step (1) is as follows: (11) Obtain energy system parameters, natural environment parameters and social environment parameters; (12) Based on the energy system parameters, natural environment parameters and social environment parameters, the energy element model E is constructed to determine the energy equivalent of the energy element. ; (13) According to the energy equivalent of energy element , build an energy right circulation price model based on energy units, and determine the price range in energy unit circulation .

3. The energy system optimization and control method based on energy element according to claim 2 is characterized in that: Energy Metamodel E for ； ； In the formula, A is the property set of the energy element, including the physical property set of the energy element P and the economic attribute set W ; X is a set of influencing factors; are influencing factors, which represent the influencing factors of energy use system, energy form, natural environment and social environment respectively. n is the number of influencing factors; R is the attribute set of energy element A and the set of influencing factors X A set of mapping relationships between them; For X arrive P The mapping function of For X arrive W The mapping function of .

4. The energy system optimization and control method based on energy element according to claim 3 is characterized in that: Energy Equivalent of Energy Element for ； ； In the formula, is the energy equivalent of the energy element, in kWh; The energy equivalent of the energy element The evaluation function of Heterogeneous Energy i Energy equivalent conversion function of Heterogeneous Energy i The circulation volume, in J; I It is a collection of heterogeneous energy contained in the energy element; P A collection of physical properties.

5. The energy system optimization and control method based on energy element according to claim 4 is characterized in that: The physical properties of energy elements P for ； ； ； In the formula, is the physical property of the energy element; m is the number of physical attributes; It is from the collection X To physical properties 's mapping.

6. The energy system optimization and control method based on energy element according to claim 5 is characterized in that: Economic attribute collection W for ； ； ； In the formula, W is a set of economic attributes; It is the economic attribute of energy element; k is the number of economic attributes; For X To economic attributes 's mapping.

7. The energy system optimization and control method based on energy element according to claim 6 is characterized in that: Price range in energy circulation , solved by the following formula: ； In the formula, / The energy price given by the energy provider / receiver, in yuan / kWh; / It is the change of energy utility after providing / receiving energy yuan, in yuan; / The energy cost from the perspective of the energy provider / receiver, in yuan; The price lower bound for the energy yuan provider, in yuan; is the upper limit of the price for the energy yuan receiver, in yuan; U is the energy utility set of energy elements; / It is the set of energy element expenditure costs from the perspective of energy element provider / receiver.

8. The energy system optimization and control method based on energy element according to claim 1 is characterized in that: In step (2), the user energy optimization control model with energy unit price range and energy supply and demand balance as constraints and minimum user energy cost as the objective function is: ； ； ； ； ； ； In the formula, C Energy cost for users, t To control the time period sequence number, T To control the number of time slots, e is the serial number of the energy element type, E is the number of energy element types, To control the length of the time period, for t The power purchased by the user from the power grid during the period, , Respectively represent the energy element received in the energy right circulation e Power and energy supply e The power, for t The unit energy price of electricity purchased by users from the power grid during the period, for t The energy price per unit of energy received by the user in the energy rights circulation during the time period, for t The energy price per unit of energy is provided in the circulation of energy rights of users during the period. For users t Energy demand during the period, and Separate users t Fixed energy demand and adjustable energy demand in different time periods, for t The maximum upward / downward adjustment capability of the user within the time period.

9. The energy system optimization and control method based on energy element according to claim 1 is characterized in that: In step (3), mixed integer linear programming, intelligent algorithm and other methods are used to solve the user energy consumption optimization control model to obtain the energy element circulation situation and the corresponding optimal user energy consumption strategy.

10. The energy system optimization and control method based on energy element according to claim 1 is characterized in that: In step (4), the power system network flow model is solved with the minimum voltage deviation as the objective function to obtain the optimal power flow distribution of the power system network: ； ； ； ； In the formula, , for t Time injection node j The active and reactive power, , for t Time period node j The active and reactive power generation power, , for t Time period node j To Node l The branch is at the head end node j The active and reactive power at , For Node j The actual energy demand of users, , Node i To Node j The branch resistance and reactance, Node j The head node set of the terminal node; Node j is the set of end nodes of the first end node, , They are t Time period node i and nodes j The square value of the voltage, is the square value of the reference voltage, is the total system voltage deviation.

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